{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Big O Notation\n",
    "In this lecture we will go over how the syntax of Big-O Notation works and how we can describe algorithms using Big-O Notation!\n",
    "\n",
    "We previously discussed the functions below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# First function (Note the use of xrange since this is in Python 2)\n",
    "def sum1(n):\n",
    "    '''\n",
    "    Take an input of n and return the sum of the numbers from 0 to n\n",
    "    '''\n",
    "    final_sum = 0\n",
    "    for x in xrange(n+1): \n",
    "        final_sum += x\n",
    "    \n",
    "    return final_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def sum2(n):\n",
    "    \"\"\"\n",
    "    Take an input of n and return the sum of the numbers from 0 to n\n",
    "    \"\"\"\n",
    "    return (n*(n+1))/2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we want to develop a notation to objectively compare the efficiency of these two algorithms. A good place to start would be to compare the number of assignments each algorithm makes.\n",
    "\n",
    "The original **sum1** function will create an assignment **n+1** times, we can see this from the range based function. This means it will assign the final_sum variable n+1 times. We can then say that for a problem of n size (in this case just a number n) this function will take 1+n steps.\n",
    "\n",
    "This **n** notation allows us to compare solutions and algorithms relative to the size of the problem, since sum1(10) and sum1(100000) would take very different times to run but be using the same algorithm. We can also note that as n grows very large, the **+1** won't have much effect. So let's begin discussing how to build a syntax for this notation.\n",
    "________\n",
    "\n",
    "Now we will discuss how we can formalize this notation and idea.\n",
    "\n",
    "Big-O notation describes *how quickly runtime will grow relative to the input as the input get arbitrarily large*.\n",
    "\n",
    "Let's examine some of these points more closely:\n",
    "\n",
    "* Remember, we want to compare how quickly runtime will grows, not compare exact runtimes, since those can vary depending on hardware.\n",
    "\n",
    "* Since we want to compare for a variety of input sizes, we are only concerned with runtime grow *relative* to the input. This is why we use **n** for notation.\n",
    "\n",
    "* As n gets arbitrarily large we only worry about terms that will grow the fastest as n gets large, to this point, Big-O analysis is also known as **asymptotic analysis**\n",
    "\n",
    "\n",
    "As for syntax sum1() can be said to be **O(n)** since its runtime grows linearly with the input size. In the next lecture we will go over more specific examples of various O() types and examples. To conclude this lecture we will show the potential for vast difference in runtimes of Big-O functions.\n",
    "\n",
    "## Runtimes of Common Big-O Functions\n",
    "\n",
    "Here is a table of common Big-O functions:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table>\n",
    "<tr>\n",
    "    <th><strong>Big-O</strong></th>\n",
    "    <th><strong>Name</strong></th>\n",
    "</tr>\n",
    "<tr>\n",
    "    <td>1</td>\n",
    "    <td>Constant</td>\n",
    "</tr>\n",
    "<tr>\n",
    "    <td>log(n)</td>\n",
    "    <td>Logarithmic</td>\n",
    "</tr>\n",
    "    <tr><td>n</td>\n",
    "    <td>Linear</td>\n",
    "</tr>\n",
    "    <tr><td>nlog(n)</td>\n",
    "    <td>Log Linear</td>\n",
    "</tr>\n",
    "    <tr><td>n^2</td>\n",
    "    <td>Quadratic</td>\n",
    "</tr>\n",
    "    <tr><td>n^3</td>\n",
    "    <td>Cubic</td>\n",
    "</tr>\n",
    "    <tr><td>2^n</td>\n",
    "    <td>Exponential</td>\n",
    "</tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's plot the runtime versus the Big-O to compare the runtimes. We'll use a simple [matplotlib](http://matplotlib.org/) for the plot below. (Don't be concerned with how to use matplotlib, that is irrelevant for this part)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10cec7cd0>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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sK1c9iRkMvxfXWzUd2zMunII1tanJEYnEhtzcXAoLC+ncuTNQs3xvq1atAMjJ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\nG8egQYP4wx/+gNVq5euvvz7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HJMJVh+pJNHVLGl+Z16DQDXYrdM5UUnIwIxhk/8I7IBgg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\n2cwHKHS48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sZp71l3JEamRDzVo5C8M/gI9JftsGgUZ5bPze1Ung48rDbJdgghRL5JUDLE\nJdo6aNlyEoDS9dUUTBp+d4P1UAvBX/8FscP/CYC1fBHuT3wL68TanOOuRxQvN+scbktfXDjM8JFJ\nGhsmaNikiH1EU0pxuf08+y6/xv5z73Pd15Td57C5WFy1hvtmbmD+9BXYrb03zxTDmzJU10pWvgiB\ntm5L6bZHiYYTfZ7vKrKn+3bc0qHcU+LAVWjHJDc0hBBiSJOgZAgzEiluvHQUldRxzRpP8cLh1UtB\nGQbRfT8h+MrfoiJ+sDooevQvcK39Eiat687k9Yji9cs6B1rTFQJWDTZM0HhooobLKhcSI5Vh6Jy9\ncpz9malZbaEb2X1FDg9LZ6xj6cwNzJ16HxazNY8jFQMlEU/1qOnorPUI+qLofWQ7zBat16DD7XXg\nLnFgs8nHmRBCDGfyV3yIUkpx87XjJNvCWEtdjH1o9rCqo0hePUVg85+SvHgAAFv1etxP/wOWMdOy\nx9wajJhNsLJM49HJmqyoNUIlUnFOXNzHwfqtHGrYTjDiy+4rKvCysvYh7puxnpopCzFr8udpuFGG\noiMU7zXoCLRHifST7XAW2rJBR+fyuR5vutajsEiyHUIIMZLJp/4Q5dtRT+R8C1qBhfG/sRBtmNwF\nNOIddGz5BuFtz4GhoxWPo/ip/0HBwt/IBlW3C0YenqRRWiAXHSNNRyzI0YadHDi/laONu4kno9l9\nZZ5J3DdjA8uqH4BIATOqZuRxpOJOJBIpAtlgI/OYWT434Iuip4zbnms2m3B7nbi7BRudBeZurwOb\nfXj8nRNCCDHw5BNgCOo4fRX/vgtgMlH2xAKsXle+h9QvpRTxE68TeOHr6UJ2kwnn/V+k6NG/QHMU\nA3AlrNhyReegBCMjXlvoBgfrt3Gwfiunmw/m9BCZPq6GJTPWsXTGOqaMqcoGqw0NDfkaruimM9vR\nPdjoXlge6egn2+Gy9ajp6FzVSrIdQgghbkeCkiEmds1Py5b0sqelG6pxTivN84j6l2prIvjC14mf\nehMA65QFFD/9D9jKFwJwPmjw1hWD4770fHEJRkYepRSX2xo5WL+VA/Vbabx+Oruvs4fI0hnrWDJj\nLWOKJ+RxpAIgmdCzBeS3Bh1BX5RUP9mOYm9XoJENOjKrWkm2QwghxN2QT48hJBWKcePXR1G6QdG8\nyUO+sF0lY4S3Pkvorf8NySimgiKKHvtrnKs+DyaNE+0Gb14xOB9KByNWDVaVaTw4UYKRkcAwdOqv\nnuBA/VYO1m/lur+r43V66d6VLJmxjkUVqyl0uPM40tFHGYpoOMXli75eO5WHQ/E+z3e4bOlgw+vs\n0b+jsLgATbIdQgghBpgEJUOEkUhx/ddH0MNxCqZ4GbNx1pAtbFdKET/1JsEX/xK99QIABQs/RvFT\nfwfF49jfqnjrSoorkfTxTjOsm6CxbrxGsW1oviZxZxKpOCcv7U8Xqp/fTiDSnt1X5PCwuGotS2es\nY+7U+7BZC/I40pEvmdTTtR2+CP62SNdSupnsRzrbcaHXczWzCbenl2aBmUDEXiAfDUIIIQaXfPIM\nAcowuPHKMRI3gljcDsY9uQCTeWh2bE/dPE/whT8nfuZdACzjqyn+jf+JqlrLtpsG755P0Za5Ceu2\nwcYJGveP1yiQPiPDVjgW4kjDjt4L1d2TMtOy1lM9aR6aJk3oBopSinAo3m0J3dyldPvLdtjsGiVj\ni3rtVC7ZDiGEEEONBCV5ppSi9e06oo2taA4rE55ejNlpy/ewejBiITre+gfC254FPYmpoJiiR/6M\n8JJneOmmxu6DKWKZWuZxBfDQJDP3jTVhlQufYelm4CqHz2/n0PntPQrVp5VVZwOR8rFVQzajNxwk\nkzrBXvt2pDMgqeTtazs0LV3b4em2fG73Ph6XrzRRWVk5iK9GCCGEuHsSlOSZf28joeOXMVk0xv/G\noiG30pYyDKKHfknolb/BCN4Ak4mC5Z/mxtq/4deBYo4fUyjSF04zik1smKAxv8SEJheqw4qhDBqu\nneLQ+e0cbthOU8v57L50ofoSlsxYx5KqdYx1S6H6nVJKEelI3FLTEcHflg46OoJ9ZzsKHNYewUbn\nqlZFxXa0IZpRFUIIIT4oCUryKHTqKr6d6Yu/ssfmUTDJk+cR5Uo0HSH4wp9lGyCapi2j/iPfYXti\nCs0XABQWEywZY2LDBDPlhRKIDCexRJSTl/alA5HGnQTCbdl9DpuL+dNXsLhqLQsqVlLkGFq/m0NJ\nKqkT8EVv06k8Siqp3/ZcTTNR7HH0uoSu2+ugwCGd7IUQQowOEpTkSeRCKy1bTgJQuqEG18xxeR5R\nF913meCr/53YoV8C4C9bxIn1/8h+cw0dmQbcRVZYM05jzXgNtxSvDxvtoRYON+zg0PltnGw6QDLV\ndad+TPEEllStYVHVGmqnLMZilgtiyGQ7wome06syq1rdSbajeyG5p8SZmW7loKi4QLIdQgghBBKU\n5EXsso8bLx4BQ+FeMhX34qn5HhIARixIxzvfJLztWYxUknOTH+fokj/nnK0SpUyQgiku2DDBzJIx\nUi8yHCiluHTzHIfOb+NQw46c/iEAlRNms6RqLYur1uQ0MhxtUimDYG99OzKrWSUTt892mDQTxZ6C\nTLDRs3+HZDuEEEKI/klQMsjiN4Jcf+EwKmVQOGcSJeuq8z0klJ4isvcndLzx9wRSGodnfIUj1V8g\nYC0ByE7RWjNeY3qhadReuA4XyVSCU00HOdyQLlRvC93I7rNZ7MydtpzFlfezsHI13sKxeRzp4FFK\nEQ0nc6dX+bqCj1AwBur259sLLL10KE8/Frsl2yGEEEJ8WBKUDKJEe5hrvzqEEU/hmjmOsR+pzesF\nvlKKeN07+F/+W86aJnJk3jc4N+lhDFN6WdeyAlgzXmP5WI1CqwQiQ1kw4uNI404Ond/O8Qt7iSUj\n2X1e1xgWVt7P4qo1I7p/SCplEPT3nF7VWWB+J9mO3KCja7qVZDuEEEKIe0uCkkGSCka5tvkgRiSB\nY1opZY/Nw6Tl7+5q8spJGt58jv1UcHzpz+hwjAdAAxaWmFg7XmOmW1bRGqo6p2UdadzJ4YadnL92\nEqW6lo+dWjaTxZVrWFy1hunjZ6GZhv+dfKUU0UiyZ4fyzDSrUKDvbIfNbsFT2i3o6DbVqshTgFmy\nHUIIIUTeSFAyCFKhGFd/cQA9FMM+0cO4jy7AZMnPBVDCd5U3X9zBAW0ml2v+Mbu9rECxsszM8jIN\njxSuD0mxRIQTl/ZxpGEnRxp34etoye4zaxZmT72PxVVrWVR5/7BdtlfPZDtyajraOms7IiTifWQ7\nTFDkdXQLNnIzHgUOq0w9FEIIIYYoCUrusVQwytVfHCDlj2IrK2L8xxeh2Qb3bTeU4szVNnacbuCE\nfT6psuUA2Iw4S8aYWDXRQUWR1IoMRdd9zRxu2MGRxp3UNR8mpSez+7yuMSyoWMXCytXMnboMh31o\n9bjpjVKKWDSZnl7VFskWkvvbI7TdDBIN16P6zHaYu9V2OHNWtSr2OCTbIYQQQgxTEpTcQzkBybji\ndLf2gsGZm66UojkM+65FOXg9SsDsBtciACpi51k9fSyLy0uxmyUQGUpSepK65sMcbtjJ0cZdXPNd\nyu4zYWLGxLksrFjNwsrVTCurHpKBpJ4yCAaiudOruhWYJ+Kp255rMtGttiMTdHiduDPTriTbIYQQ\nQoxMEpTcI6lglKs/P0AqkAlINi0ZlIDkZlRxoNVg/02dG3ETYAWzFU/HJRbFjlM1voxFG1bf83GI\nO+fraOFI4y6ONOzkxMV9OUXqLnsR86avYGHlahZMX0mx05vHkaZ1ZjtuDTYC7RH8vighf7TPbIfV\nZk7XdniduEszQUeJg2BHK3PmVWPO09RGIYQQQuSPBCX3QDIQ5dov0gGJfXwx45++twFJIKE42Gpw\noFVxsaPzatCEM9bK7OaXWKQaqV33W9infYyGhoZ7Ng5xZwxDp+H66UxtyE4u3DiTs3/KmEoWVKxm\nUeVqZk6ah1kb/H+mum4Q8se6gg5f7qpW8djtsx2YoMhd0GN6Vee0K4ez92xHQ0NQAhIhhBBilJKg\nZIAl2jq4tvkgekf8ngYkvrjiaLvB4TbF+aDKLjpkS4Wpufwac5teYKY1iOfxv8Re/aUB//nigwlG\nfJy4uI+jF3ZztHEXoag/u89qsTOnfCkLK1ezsGL1oBWpp2s7ek6vCrRHCAZiKOP26Q6rzZwNNm5t\nFljscWCR4EIIIYQQH4AEJQMofj2Q7kMSTVIw2cv4jy1Esw9cQNIWUxzJBCKNoa4LRrPSqWrZwZyG\n/6D66ls4xlVQ9OjXsc99TObf54lh6Jy/dopjF3Zz9MJuGq+dRnVbr3ZM8QQWZYKQ2eVL7knvEEM3\nCAZit/TtyDQNbLuzbEc2w+HNXc3K4ZLaDiGEEEIMHAlKBki0qY3rLxxBJXWcFWMpe3I+mtX8oZ+3\nJaY43JYORC51dF3UWk2KaqOZ6hPPUdWwmYJkCMvE2RR99nvY5zya1x4oo5Wvo4VjF/Zw7MIejl/c\nSzgWzO4zaxZmTVnE/GkrWFC5ismlFQNyUZ+u7cgNNgK+dOYj6O8/25EtJO82vcqTWcnKMgC/v0II\nIYQQd0KCkgEQrr/JzVeOoXSDwlkTGPvIHEx3uTSpoRQXQorjPsXxdoNr0a59Ng3meBSzW7cz5f0/\nx9qWrg+xTKil8OGvUzD3MQlGBlFKT9LYcop9l1/j2IU9XLp5Lmd/mWcSC6avYv70FcwuX0KBzfmB\nf4ahG4SCMfxtmaDjlqaBsWiyz/MLi+05wUb3R6fLJtkOIYQQQgwJEpR8SIHDl2h77wwoKF44hdIH\nZn3gC72Yrqjzp4OQkz5FqNusmgIzzPWaWFgcZ1rdT0i+8W2MwDWgMxj5rxTMfVyCkUFyM3CVY427\nOXZhNycvHchZKctmsTO7fAnzp69kQcUqxnun3NFzxmPJ3OlV3TqWB/1RjD6yHRaruUewkZ5u5cDt\nlWyHEEIIIYYHCUrukjIUbVvPEDzUBIB3VRWeFXc+Jac9rjjhMzjerjgbUKS6XXeOscPcEo35XhMV\nmo/4zu8R3vmvxCPp4mgJRgZPIhmj7vJhjjbu4diF3Vxtv5izv6xoMkur1zK/YiU1kxdis9h7PIdh\nKELZ2o5umQ5fFH9b5I6yHW6vE0+pI1Pb0TXdylko2Q4hhBBCDH8SlNwFI5Hi5msniJy/CWYTYx+e\nQ1HtxD7PSeiK8yHFKZ/itD93WpYJqCgyMc9rYq5XY6ITDP8VOt77Du17f4JKpO/GW6cvo3Dj17DX\nPigXoveIUorLrQ0cv7iP4xf3crr5EMlUPLvfYXMxd9oy5k9fwfzpKwi0RKisrCQeS+G/GcHf7ssG\nG+npVplsh95XtkO7pZC8K+NR7HVglWyHEEIIIUY4CUo+oFRHjBu/PkL8ehCtwMK4pxbimFLS4zil\nFNejcNpvcNqvOBdUJI2u/XYNajwm5ns15nhNFNvSQUbqxjkCL32T6MFfgpGex2WvfZDCB/4EW+WK\nQXmNo42/o5Xjl/Zx4uI+Tl7chy/cmrN/+rga5k1bwcyyJZRYp9PhT+K/EWFv3Q1uXPOxJXyRaKTv\nbIeryJ47vSq7qpUDV5FdgkwhhBBCjGoSlHwAsSt+brx0BD2cwOJ2MP43F2MrcWX3h5KKcwFFXcDg\ntE/Rnsg9f4oLZns0aj0mKopMWLT0hahSinjDbsJbnyV+8nVQCkwaBQs/RuHGP8E6ac5gvswRL56M\ncubyEY5f2MuJS/toajmfs7/Q7qW8aC5jLbMoSlYSb7Vy7XyUK7ofONLrc1osGu7OYOOW1azcXgdW\nm2Q7hBBCCCFuR4KSOxQ81kzrO3VgKAqmeBn35ALiNit17QZnA4qzAYMrkdxzCi1Q6zFRmwlEOrMh\nnZSeJHbkRcLbniXZfDS90WzDed8ncW34QyxjKwbp1Y1shjK4dOMsxy7s5WjDHuqvH0c3ujIbGlbc\nRgWueAXFySocxjhMN0yEgTBJIH2sq8iO25vbobwj6mP23CpchXZMmmQ7hBBCCCHuhgQl/VC6Qeu7\ndYSOXSahmWlfOoPrUydxth6awim6VwpYtXRtSLXbxGyPxhQXaL1MyzHCPiJ7/p3wju9nV9LSXKU4\nV30e5+pnMBePG6RXN/Ik4ikC7VEuXr3IyYv7qL95mMsdp0iojq6DlAmnPpHiVBXFqSoKU1PRsGC2\naLhLc4OObN8OrwObrec/l4aGGIXFA9/4UAghhBBiNJGgpA+B9ihHtl7kQsrG5XmLuVZUjIEJrqVD\nEc0ElYXpIGSmOz0ly9rH3fLUzfOEt3+P6P6fZYvXLeOrca39Mo7FT2OyOQbldQ1nylB0hOKZVay6\nltBtaWul2X+aFv0MQct5YubcuhCb4aE4WclYyyzK3XMpGzO2K+jwpms9Cosk2yGEEEIIkQ8SlHTT\nHlfUBxUNQcW51gTXdQuMr8ruNwFTC03UuNOBSGWRCbu574tYZejEz7xHZOcPiNe9na4XAWzV6ylc\n9xVsNRukyPkWiUQ625HTt8MXJdAWIeCPoqcMdBJ0WJoIWRoIWRoJm6+ARWV/oy2mAia5aqkau4i5\nU++jYsoMPKXOXrMdQgghhBAiv0btFZquFFcj0BA0aAgpzgcVvpzCdAtmw2BSKkrNFCdVXgszik04\nLHcWQBgdbUT2/ZTI7h+ht13KPKUdx5JNuNZ+CeuEWQP+moaLzmxHZ7DRuXxuZ/+OSEeixzkGKcLm\ny+kAxHmBkNaEQVeXSc1kpmLcbOZNX8b86cupnDAbi9k6mC9LCCGEEELcpVERlCiVDjguhBQXOxQX\nOhRNHYqEkXucQ1NMCgWY1NLKpI4gtQvHUbpq6h1nMpRSJC8dJLLzh0SPvgiZ/hbmknKcq34Xx7JP\nYS4cM9Avb0hKJvRsn47emgbqKeO255rNJoo8dihuI2hp5GbyDFdDZ0josewxJkxUjJvF7KlLmV2+\nlJrJCyiwOQfjpQkhhBBCiAE2IoOSmK641KG4EEoHIBdDikAvbSTGFsD0wvQ0rPGXr2PdeRpSBha3\ng7In5lMwwX1HP8+Ih4kd/hXhXT8idfl4eqPJhL32IZyrn8FeswGTNrKWhFWGItwRv23QEQ7F+zzf\n4bKl+3ZkmgYWlzhIWFq53HGKhpaj7Gk+RNgXzDlncmlFJghZQm35EgoLiu/lSxRCCCGE+MCUUmAY\nKN1ApXSUoacfdQOlZx5TqW5f6yijc1vmmJzj9W7H6Lnbbj2m+/m9/ly92SFCugAAGRhJREFU7+fs\n85hbf5be62ss+t5f3tX7NuyDkmhKcTmsaMr819yhuBaFW/tnOy0wrdDE9EIT04rSj4VWE8lAlJYt\nJ4k1tQNQOHsiYx6oQbP3PfUnnRU5RGTf88QOv4CKp1d3MrlKcC7/HZwrP4eldOq9eMmDJp3tuCXo\nyEyzCrRHSPWR7dDMJtyeXpoFZh6tNjPXfc3UNR/iSNNBTu07gD/clvMcZe5JzC5fkg1EvIVj7/VL\nFkIIIcSHpJTKvXBN5X5tZL9O5R7TY3/n9lQ/+3OPu/3+7ueneuwPh0K02+y3OT/V+8+/9bjM96NZ\n0V2eN6yCko5kV+DRFFY0hxU3Yz2PM5tgsrMr+JheZKKsgJxpWEopgscv0/b+GVRCR3PaGPtQLa4Z\nfS/Ha3S0ETn4C6J7nyd1/Ux2u3XaUpyrn8Ex/0lM1uGxRKxSinAoTsAXzc10ZArM+812OK3ZJXNv\nDT4KiwvQtNz3+2r7RY417aDuwGHqmg/16JzucZUyu3wps6cuZU75Uso8k+7J6xZCCCEGS/YCPZm+\nEDaSXRfjRjJzoZt5NJKpvvd1/1rX8V+7xiXP4a6L5d4u5LtfjN/2Qj3Vz/7u5/ceSHTuJ3M3fbiK\n9H/IHTNZLZjMGiZz5tFixqRlHs2Z/yzmzDHdtpm7H9N53i3HWno5vtdjbvPzezu+85w+j9EwWSy3\n/Nzc13Sm7fpdvV/DKij5vw+kemyzmGCS00R5oYkpLpjiMjHZ1ffSvIn2MK1vnSLW7APAOaOMsQ/W\nYnbZez1eGTrxs+8T3fs8sZNvgJ6eC6YVjsGx9LdwLPttrONrBuAVDrxkUifo6wo2Ll1o4fguf3ra\nlS9CKtlHtkMzUex15AYd3ZoH2gtun00ylEFzSwOnm9MBSF3zYQKR9pxjip1eaiYvorZ8MXPKlzKp\ndLqsRCaEEKNMzl31VO5Fu0ql0nelcy7au11Ep1L97+v8+g72GZnAIR1A5D5n7wFEH/s6v7/Hd81b\n7umz372cC2fLLV+bzWg52yw9jtN6Oadrv6Wf/d3Pt/Szv+u46y03mThlcj8/39L7/lsv4DUt3/8L\n8mc0BCU2DSa7TJR3/ldoYoIDzHfYW0KlDPz7GvHtawRdoTmslG6ooXDWhF4vhpPXThM9sJno4V9h\n+K+mN5o07LUP4lj2aQpmfwSTxTaQL/EDU0oR6UjcUtMRwd+WDjo6gneW7bi1U7m7xEmROzfb0Zd0\nEHKe05kApK75EKFoIOcYj6uUWVMWM2vKImqnLJYgRAghPqTud9eNRKrrAjmVwkgku+64Z7/P7E/q\nGMlk5qI5vS17Jz57TDIbIHR9n+q6eO/2vUp2e45u36tkKhsAdF78p+IJGg1j0C7ah4KcC1pL5oLW\nmrm4tVpu2Wfpe1/me81qIRgO4ynxZo/PPvdtLuT73m/u+hm3bu9+572vi/LuzzsMP99DDQ2MqazM\n9zBGrWEVlPzTsv+/vTsNkuOs7zj+nenpOfaaPaSV1pJsHZYP2cLygSEYYogVwAGMkyqemCIFIceb\npFJOXiQVUkneUQGqUgGSSiqcBQngPFyGXEBwLhLC4UNYPiRrV1ppL2m19zVHz5EX3TM7s4e0Wlbq\nndXvUzXVxzy9+9eUvfP8+nm6O7biE9LXYqF/nPGnXsabmAeg9fAuOh+8BSdVHyqKU8Nknv0qmae/\nTGH4hep+p2svqde8h6b7H8O5xtOKCl5wbUfN7XOnap7jUfBW/4MejUZoa09Vw0ahnOHAwd3VIJJM\nre+2uaVSkbOjr/DSwDO8NPAsJwafYz5bf2F6Z0s3t++5h9v33MuhPffQ07n2O5mJiFwr5VIp6ER7\nlPL+spz3O9ylvEf2TD9T01m/Ux/s89sExywJBZUOeCUElL3gbHzeq++g5wurbldCRHWUoC5UFKvb\nlWdfNbqI4xBxgzPgbtCpre2Y164vfa/SYb/EexHXIerE6n9HpYPtxoguO26x47/8Z17+vfpwcfXO\nmvf19XFAnWjZIhoqlKwnkHiT84z/5yss9I4C4HY2s+3Nh0jt6ay2KWVnyP7kn8g882Xyp/67+kc+\n0tRO6sijpO4zuHvvv2p/VCqjHUvDRuUC88uNdiRT7rILydMdTbR3pWhtSxJ1Fuv2/4DtvOIac16G\n3pEXOTl4jJNDx3hl6Hky+fm6NtvadtaNhOxo360QInIdKxeLlHJe0Nn3ajr+tZ37oLNdGwhWCQe1\n++tCQT44ix+0LXvemn5vuRIu1nCmfuAafF7rEokQjbtBJznoLAfbEdef4lK/HSMSj1U7zFHX9Tvs\ndduV950l27HFgBB0yivH1/7eShiIxmP128GxZwfOsf/gwcUOfawxz6qLyMZqqFByJUq5ApP/18f0\nM2ehVCbiOnT8zH7S9+4lEotSys2Re/E7ZI89Sfbl74IXXDHvxEne+RZS9xoSh44Sia18ncmVKhRK\nzKz03I7gblZefvUvxUg0Qlt7cvGajq6m6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LUTxpg/B84BGeDb1trvhlxWo3oB+GAw3SiL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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a777950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from math import log\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.style.use('bmh')\n",
    "\n",
    "# Set up runtime comparisons\n",
    "n = np.linspace(1,10,1000)\n",
    "labels = ['Constant','Logarithmic','Linear','Log Linear','Quadratic','Cubic','Exponential']\n",
    "big_o = [np.ones(n.shape),np.log(n),n,n*np.log(n),n**2,n**3,2**n]\n",
    "\n",
    "# Plot setup\n",
    "plt.figure(figsize=(12,10))\n",
    "plt.ylim(0,50)\n",
    "\n",
    "for i in range(len(big_o)):\n",
    "    plt.plot(n,big_o[i],label = labels[i])\n",
    "\n",
    "\n",
    "plt.legend(loc=0)\n",
    "plt.ylabel('Relative Runtime')\n",
    "plt.xlabel('n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note how much of a difference a Big-O efficiency can make for the same n value against the projected runtime! Clearly we want to choose algorithms that stay away from any exponential, quadratic, or cubic behavior!\n",
    "\n",
    "In the next lecture we will learn how to properly denote Big-O and look at examples of various problems and calculate the Big-O of them!"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:python3]",
   "language": "python",
   "name": "conda-env-python3-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
